Neurobiologically Based Stratification of Recent Onset Depression and Psychosis: Identification of Two Distinct Transdiagnostic Phenotypes

Paris Alexandros Lalousis*, Lianne Schmaal, Stephen J. Wood, Renate L.E.P. Reniers, Nicholas M. Barnes, Katharine Chisholm, Sian Lowri Griffiths, Alexandra Stainton, Junhao Wen, Gyujoon Hwang, Christos Davatzikos, Julian Wenzel, Lana Kambeitz-Ilankovic, Christina Andreou, Carolina Bonivento, Udo Dannlowski, Adele Ferro, Theresa Liechtenstein, Anita Riecher-Rössler, Georg RomerMarlene Rosen, Alessandro Bertolino, Stefan Borgwardt, Paolo Brambilla, Joseph Kambeitz, Rebekka Lencer, Christos Pantelis, Stephan Ruhrmann, Raimo K.R. Salokangas, Frauke Schultze-Lutter, André Schmidt, Eva Meisenzahl, Nikolaos Koutsouleris, Dominic Dwyer, Rachel Upthegrove

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Background: Identifying neurobiologically based transdiagnostic categories of depression and psychosis may elucidate heterogeneity and provide better candidates for predictive modeling. We aimed to identify clusters across patients with recent-onset depression (ROD) and recent-onset psychosis (ROP) based on structural neuroimaging data. We hypothesized that these transdiagnostic clusters would identify patients with poor outcome and allow more accurate prediction of symptomatic remission than traditional diagnostic structures. Methods: HYDRA (Heterogeneity through Discriminant Analysis) was trained on whole-brain volumetric measures from 577 participants from the discovery sample of the multisite PRONIA study to identify neurobiologically driven clusters, which were then externally validated in the PRONIA replication sample (n = 404) and three datasets of chronic samples (Centre for Biomedical Research Excellence, n = 146; Mind Clinical Imaging Consortium, n = 202; Munich, n = 470). Results: The optimal clustering solution was two transdiagnostic clusters (cluster 1: n = 153, 67 ROP, 86 ROD; cluster 2: n = 149, 88 ROP, 61 ROD; adjusted Rand index = 0.618). The two clusters contained both patients with ROP and patients with ROD. One cluster had widespread gray matter volume deficits and more positive, negative, and functional deficits (impaired cluster), and one cluster revealed a more preserved neuroanatomical signature and more core depressive symptomatology (preserved cluster). The clustering solution was internally and externally validated and assessed for clinical utility in predicting 9-month symptomatic remission, outperforming traditional diagnostic structures. Conclusions: We identified two transdiagnostic neuroanatomically informed clusters that are clinically and biologically distinct, challenging current diagnostic boundaries in recent-onset mental health disorders. These results may aid understanding of the etiology of poor outcome patients transdiagnostically and improve development of stratified treatments.

Original languageEnglish
Pages (from-to)552-562
Number of pages11
JournalBiological Psychiatry
Issue number7
Early online date12 Apr 2022
Publication statusPublished - 1 Oct 2022

Bibliographical note

CC BY 4.0


  • Clustering
  • Depression
  • Machine learning
  • Nosology
  • Psychosis
  • Transdiagnostic


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